Sparse modeling approach for quasiclassical theory of superconductivity

Abstract

We propose the sparse modeling approach for quasiclassical theory of superconductivity, which reduces the computational cost of solving the gap equations. The recently proposed sparse modeling approach is based on the fact that the Green's function has less information than its spectral function and hence is compressible without loss of relevant information. With the use of the so-called intermediate representation of the Green's function in the sparse modeling approach, one can solve the gap equation with only 10-100 sampled Matsubara Green's functions, while the conventional quasiclassical theory needs 100-1000 ones. We show the efficiency of our method in bulk and vortex states, by self-consistently solving the Eilenberger equations and gap equations. We claim that the sparse modeling approach is appropriate in all theoretical methods based on the Matsubara formalism in the quasiclassical theory of superconductivity.

10 pages, 5 figures

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Cited by in corpus (3)

• sparse-ir: optimal compression and sparse sampling of many-body propagators
• Efficient anisotropic Migdal-Eliashberg calculations with the Intermediate Representation basis and Wannier interpolation
• Low rank Green's function representations applied to dynamical mean-field theory